MSK SPECTRUM data freeze: major cell type embeddings
Florian Uhlitz
2020-12-11
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
theme_cowplot2 <- function(...) {
theme_cowplot(font_size = 16, font_family = "sans", ...) %+replace%
theme(strip.background = element_blank(),
panel.background = element_rect(fill = "transparent", color = NA),
plot.background = element_rect(fill = "transparent", color = NA),
panel.border = element_blank())
}
theme_set(theme_cowplot2())
remove_xaxis <- theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank())
remove_guides <- guides(color = F, fill = F, shape = F, alpha = F)### load all data ---------------------------------
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v5 <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_major.yaml")
helper_f2 <- function(x) select(unnest(enframe(x, "subtype", "gene"), cols = gene), gene, subtype)
markers_v5_super <- lapply(yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_super.yaml"), helper_f2)
clrs <- yaml::read_yaml("/home/uhlitzf/spectrum_tme/_data/small/signatures/hgsc_v6_colors.yaml") %>%
lapply(function(x) map_depth(x, vec_depth(x)-2, unlist))
clrs$patient_id_short <- clrs$patient_id
names(clrs$patient_id_short) <- str_remove_all(names(clrs$patient_id), "SPECTRUM-OV-")## load meta data
meta_tbl <- read_excel("/home/uhlitzf/spectrum_tme/_data/small/MSK SPECTRUM - Single cell RNA-seq_v6.xlsx", sheet = 2) %>%
filter(!(patient_id %in% c("SPECTRUM-OV-100", "SPECTRUM-OV-099")),
therapy == "pre-Rx") %>%
rename(sample = isabl_id) %>%
mutate(patient_id_short = str_remove_all(patient_id, "SPECTRUM-OV-"),
sort_short = str_remove_all(sort_parameters, "singlet, live, "),
tumor_supersite = str_replace_all(tumor_supersite, "Upper Quadrant", "UQ")) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
## load wgs meta data
signature_tbl <- read_tsv("/home/uhlitzf/spectrum_tme/_data/small/mutational_signatures_summary.tsv") %>%
select(patient_id, consensus_signature) %>%
bind_rows(tibble(patient_id = unique(sort(meta_tbl$patient_id[!(meta_tbl$patient_id %in% .$patient_id)])), consensus_signature = "NA")) %>%
mutate(consensus_signature = ordered(consensus_signature, levels = names(clrs$consensus_signature))) %>%
arrange(consensus_signature) %>%
distinct(patient_id, .keep_all = T)
meta_tbl <- left_join(meta_tbl, signature_tbl, by = "patient_id")## load full seurat objects with expression data
# seu_obj_tc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/T.cell_processed_filtered_sub.rds")
# seu_obj_cc <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v5/Ovarian.cancer.cell_processed_filtered.rds"))
seu_obj_cc <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_processed_filtered.rds")
markers_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc_markers_02.tsv")plot_data <- cbind(cell_id = colnames(seu_obj_cc), FetchData(seu_obj_cc, c("umapharmony_1", "umapharmony_2", "umappca_1", "umappca_2", "RNA_snn_res.0.2", "sample", "cluster_label", grep("pathway", colnames(seu_obj_cc@meta.data), value = T)))) %>%
as_tibble() %>%
left_join(meta_tbl, by = "sample") %>%
filter(!is.na(consensus_signature))
base_umap <- ggplot(plot_data) +
coord_fixed() +
NoAxes() +
theme(legend.position = c(0, 1),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = margin(1, 1, 1, 1),
#panel.border = element_rect(linetype = 1, color = "black", size = 1),
legend.text = element_text(size = 14, margin = margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 22))
arrow <- arrow(angle = 20, type = "closed", length = unit(0.1, "npc"))
umap_coord_anno <- ggplot(tibble(group = c("UMAP1", "UMAP2"),
x = c(0, 0), xend = c(1, 0),
y = c(0, 0), yend = c(0, 1),
lx = c(0.5, -0.15), ly = c(-0.15, 0.5),
angle = c(0, 90))) +
geom_segment(aes(x, y, xend = xend, yend = yend, group = group),
arrow = arrow, size = 1, lineend = "round") +
geom_text(aes(lx, ly, label = group, angle = angle), size = 4) +
theme_void() +
coord_fixed(xlim = c(-0.3, 1), ylim = c(-0.3, 1))
add_umap_coord <- function(gg_obj) {
p <- ggdraw() +
draw_plot(gg_obj, x = 0, y = 0, width = 1, height = 1) +
draw_plot(umap_coord_anno, x = -0.015, y = -0.02, width = 0.2, height = 0.2)
return(p)
}pt.size <- 0.1
pt.size.mini <- 0.01
pt.alpha <- 0.05
pt.alpha.mini <- 0.02
median_tbl <- plot_data %>%
group_by(patient_id_short) %>%
summarise(umappca_1 = median(umappca_1),
umappca_2 = median(umappca_2))
umap_pca_mutsig <- base_umap +
geom_point(aes(umappca_1, umappca_2, color = consensus_signature),
size = pt.size, alpha = pt.alpha) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Mutational signature")
umap_mutsig <- base_umap +
geom_point(aes(umapharmony_1, umapharmony_2, color = consensus_signature),
size = pt.size, alpha = pt.alpha) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "")
umap_pca_cluster <- base_umap +
geom_point(aes(umappca_1, umappca_2, color = cluster_label),
size = pt.size, alpha = pt.alpha) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.cell) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Cluster")
umap_cluster <- base_umap +
geom_point(aes(umapharmony_1, umapharmony_2, color = cluster_label),
size = pt.size, alpha = pt.alpha) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.cell) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "")
cluster_legend <- cowplot::get_legend(umap_pca_cluster)
umap_pca_jak_stat <- base_umap +
# geom_point(aes(umappca_1, umappca_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
geom_point(aes(umappca_1, umappca_2, color = JAK.STAT.pathway),
size = pt.size, alpha = pt.alpha,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
labs(title = "JAK-STAT signaling")
umap_jak_stat <- base_umap +
# geom_point(aes(umapharmony_1, umapharmony_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
geom_point(aes(umapharmony_1, umapharmony_2, color = JAK.STAT.pathway),
size = pt.size, alpha = pt.alpha,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
guides(color = F) +
labs(title = "")comp_plot_wrapper <- function(comp_tbl = comp_tbl_sample_sort, cts, y = "nrel", x = "tumor_subsite", fill = "cell_type", switch = NULL, facet = F, exclude_ascites = F, super_type = NULL) {
if (facet == F) comp_tbl <- filter(comp_tbl, !is.na(tumor_supersite))
if (exclude_ascites == T) comp_tbl <- filter(comp_tbl, tumor_supersite != "Ascites")
if (y == "nrel") ylab <- paste0("% cells")
if (y == "log10n") ylab <- paste0("# cells")
if (y == "n") ylab <- paste0("# cells")
p <- ggplot(filter(comp_tbl, sort_short_x == cts),
aes_string(x, y, fill = fill)) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
axis.title.y = element_text(margin = margin(0, -1, 0, 1, unit = "npc")),
strip.text.y = element_blank(),
strip.background.y = element_blank(),
plot.margin = grid::unit(c(0.04, 0.01, 0.02, 0.01), "npc")) +
#scale_x_discrete(expand = c(0, 0)) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (fill != "cluster_label") p <- p + scale_fill_manual(values = clrs[[fill]])
if (fill == "cluster_label") p <- p + scale_fill_manual(values = clrs[[fill]][[super_type]])
if (facet == "tumor_supersite") p <- p +
facet_grid(sort_short_x~tumor_supersite,
space = "free", scales = "free", switch = switch)
if (facet == "consensus_signature") p <- p +
facet_grid(sort_short_x~consensus_signature,
space = "free", scales = "free", switch = switch)
if (facet == "patient_id_short") p <- p +
facet_grid(sort_short_x~patient_id_short,
space = "free", scales = "free", switch = switch)
if (facet == F) p <- p +
facet_grid(sort_short_x~.,
space = "free", scales = "free", switch = switch)
if (y == "nrel") p <- p +
geom_bar(stat = "identity", width = 1) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100")) +
theme(strip.text.x = element_blank(),
strip.background.x = element_blank())
if (y == "log10n") p <- p +
geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 1) +
scale_y_continuous(expand = c(0, 0),
breaks = c(1, 2, 3),
limits = c(0, 4),
labels = function(x) parse(text = paste("10^", x))) +
expand_limits(y = c(0, 4)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack(), width = 1) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 2500, 5000, 7500),
limits = c(0, 7500),
labels = c("", "2500", "5000", "7500")) +
expand_limits(y = c(0, 7500)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5),
plot.title = element_text(face = "plain", size = 14,
hjust = 0.5, vjust = 0.5)) +
labs(title = cts)
return(p)
}
common_label_layers <- list(
theme(axis.title.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks = element_blank(),
axis.line.x = element_blank(),
plot.margin = ggplot2::margin(0.02, 0.01, 0.02, 0.01, "npc")),
scale_y_discrete(expand = c(0, 0)),
guides(fill = F, color = F)
)
comp_label <- function(x = "tumor_subsite", y = "label_supersite", fill = "tumor_supersite", facet = F, cts = c("CD45-", "CD45+")) {
comp_tbl <- comp_tbl_sample_sort
if (facet == F) comp_tbl <- filter(comp_tbl, !is.na(tumor_supersite))
p <- ggplot(distinct(filter(comp_tbl, therapy == "pre-Rx", sort_short_x %in% cts), tumor_subsite, tumor_supersite, consensus_signature, label_supersite, label_mutsig, patient_id_short, sample_id_Myeloid.cell, sample_id_T.cell, rank_T.cell, sample_id_Fibroblast, sample_id_Ovarian.cancer.cell, rank_Ovarian.cancer.cell, sample_id_B.cell, sample_id_Plasma.cell),
aes_string(x, y, fill = fill)) +
geom_tile() +
common_label_layers
if (fill != "cluster_label") p <- p + scale_fill_manual(values = clrs[[fill]])
if (fill == "cluster_label") p <- p + scale_fill_manual(values = clrs[[fill]][[super_type]])
if (facet == "patient_id_short") p <- p + facet_grid(label_supersite~patient_id_short,
space = "free", scales = "free")
if (facet == "tumor_supersite") p <- p + facet_grid(label_supersite~tumor_supersite,
space = "free", scales = "free")
if (facet == F) p <- p + facet_grid(label_supersite~.,
space = "free", scales = "free")
return(p)
}
comp_label_boxplot <- function(comp_tbl = comp_tbl_sample_sort, x = "tumor_supersite", y = "rank_T.cell", facet = T, cts = c("CD45-", "CD45+"), clr = "tumor_supersite", ct = names(clrs$cell_type), pvals = F, exclude_ascites = F) {
if (facet == F) comp_tbl <- filter(comp_tbl, !is.na(tumor_supersite))
if (exclude_ascites == T) comp_tbl <- filter(comp_tbl, tumor_supersite != "Ascites")
# rank_tests_sub <- filter(rank_tests, sort_short_x %in% cts, pval < 0.05) %>%
# mutate(pstar = "*")
required_columns <- c("tumor_subsite", "tumor_supersite", "consensus_signature", "BRCA1_status", "BRCA2_status", "BRCA_status", "label_supersite", "patient_id_short", "sample_id_Myeloid.cell", "sample_id_T.cell", "rank_T.cell", "sample_id_Fibroblast", "sample_id_Ovarian.cancer.cell", "rank_Ovarian.cancer.cell", "sample_id_B.cell", "sample_id_Plasma.cell", "rank_IR", "sample_id_IR", "rank_MC", "sample_id_MC", "sample_cd45p", "sample_rank")
missing_columns <- required_columns[!(required_columns %in% colnames(comp_tbl))]
comp_tbl <- bind_cols(comp_tbl, matrix(rep("distinct_helper", n = length(missing_columns)), ncol = length(missing_columns)) %>% set_colnames(missing_columns) %>% as_tibble())
p <- ggplot(
distinct(filter(comp_tbl, therapy == "pre-Rx", sort_short_x %in% cts, cell_type %in% ct), tumor_subsite, tumor_supersite, consensus_signature, BRCA1_status, BRCA2_status, BRCA_status, label_supersite, patient_id_short, sample_id_Myeloid.cell, sample_id_T.cell, rank_T.cell, sample_id_Fibroblast, sample_id_Ovarian.cancer.cell, rank_Ovarian.cancer.cell, sample_id_B.cell, sample_id_Plasma.cell, rank_IR, sample_id_IR, rank_MC, sample_id_MC, sample_cd45p, sample_rank)
# distinct(filter(comp_tbl, therapy == "pre-Rx", sort_short_x %in% cts, cell_type %in% ct), -n, -log10n, -nrel)
) +
geom_tile(aes_string(x, y, fill = clr),
alpha = 0.8) +
geom_boxplot(aes_string(x, y, fill = clr),
width = 0.35, size = 2.5, color = "white", outlier.shape = NA) +
geom_boxplot(aes_string(x, y, color = clr),
width = 0.35, size = 1, fill = "white", outlier.shape = NA) +
geom_hline(yintercept = 0, linetype = 2, size = 0.5, color = "black") +
scale_fill_manual(values = clrs[[clr]]) +
scale_color_manual(values = clrs[[clr]]) +
coord_flip(clip = "off", ylim = c(-1.01, 1.01)) +
theme(axis.title.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = ggplot2::margin(0.04, 0.05, 0.02, 0.01, "npc")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0), breaks = c(-1, 0, 1)) +
guides(fill = F, color = F) +
labs(y = "Scaled rank")
if (facet == T) p <- p + facet_grid(label_supersite~patient_id_short,
space = "free", scales = "free")
if (facet == F) p <- p + facet_grid(label_supersite~.,
space = "free", scales = "free")
if (pvals == T) p <- p +
geom_text(aes(x = comparison, y = 1.05, label = pstar), data = rank_tests_sub, hjust = 0.5, vjust = 1, size = 5, angle = 90) #+
# geom_segment(aes(x = "Adnexa", xend = comparison, y = 1.07, yend = 1.07), data = rank_tests_sub)
return(p)
}
# ggdraw() + draw_plot(comp_label_boxplot(x = "tumor_supersite", y = "rank_T.cell", facet = F, cts = "CD45+", ct = "T cell", pvals = T), width = 0.9)
hist_plot_wrapper <- function(cts, x = "nrel") {
if (x == "nrel") {
xlab <- paste0("% cells")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("# cells")
bw <- 0.2
}
p <- ggplot(filter(comp_tbl_sample_sort, sort_short_x == cts, !is.na(cell_type))) +
ggridges::geom_density_ridges(
aes_string(x, "cell_type", fill = "cell_type"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~label_therapy,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cell_type) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = ggplot2::margin(0.02, 0, 0.02, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 4)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}cluster_comp <- plot_data %>%
mutate(sort_short_x = str_replace_all(sort_short, "U", "CD45-")) %>%
group_by(cluster_label, consensus_signature, sample, sort_short_x, tumor_supersite, therapy) %>%
tally %>%
group_by(consensus_signature, sample, sort_short_x, tumor_supersite, therapy) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup %>%
arrange(cluster_label != "Secretory.cell.6", -nrel) %>%
mutate(sample = ordered(sample, levels = unique(sample))) %>%
mutate(cell_type = "Ovarian.cancer.cell")
cluster_comp_rank <- cluster_comp %>%
distinct(sample, .keep_all = T) %>%
filter(sort_short_x == "CD45-") %>%
mutate(sample = ordered(sample, levels = unique(as.character(sample)))) %>%
mutate(sample_rank = scales::rescale(as.numeric(sample), c(-1, 1)))
p1 <- comp_plot_wrapper(cluster_comp, "CD45-", "n", x = "sample", fill = "cluster_label", super_type = "Ovarian.cancer.cell")
p2 <- comp_plot_wrapper(cluster_comp, "CD45-", "nrel", x = "sample", fill = "cluster_label", super_type = "Ovarian.cancer.cell")
p3 <- comp_label_boxplot(cluster_comp_rank, x = "consensus_signature", y = "sample_rank", clr = "consensus_signature", cts = "CD45-")
pcomp_grid_p_full <- plot_grid(p1, p2, p3,
ncol = 1, align = "v",
rel_heights = c(0.25, 0.25, 0.4))# cluster_comp <- plot_data %>%
# group_by(cluster_label, consensus_signature) %>%
# tally %>%
# group_by(consensus_signature) %>%
# mutate(total_cs = sum(n)) %>%
# group_by(cluster_label) %>%
# # filter(total_cs > 10000) %>%
# mutate(total_cl = sum(n),
# nnorm = n/total_cs,
# nrel = n/sum(n),
# nrelnorm = nnorm/sum(nnorm)) %>%
# gather(key, value, -cluster_label, -consensus_signature, -total_cs, -total_cl)
#
# cluster_comp %>%
# filter(key %in% c("nrelnorm"),
# total_cl > 10000) %>%
# ggplot() +
# geom_bar(aes(cluster_label, value, fill = consensus_signature),
# stat = "identity", position = "dodge") +
# facet_wrap(~key, scales = "free_x") +
# scale_fill_manual(values = clrs$consensus_signature) +
# theme(axis.ticks.y = element_blank(),
# axis.title.x = element_text(),
# axis.text.y = element_text(hjust = 1)) +
# coord_flip()cancer_grid_pca <- ggdraw() +
draw_plot(add_umap_coord(umap_pca_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_pca_cluster + guides(color = F)),
x = 0.25, y = 0, width = 0.25, height = 1) +
draw_grob(cluster_legend, x = 0.5, y = -0.15, height = 1) +
draw_plot(add_umap_coord(umap_pca_jak_stat), x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid <- ggdraw() +
draw_plot(add_umap_coord(umap_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_cluster + guides(color = F)),
x = 0.25, y = 0, width = 0.25, height = 1) +
draw_plot(pcomp_grid_p_full,
x = 0.5, y = 0, width = 0.18, height = 1) +
draw_plot(add_umap_coord(umap_jak_stat), x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid_pca
cancer_grid
# ggsave("_fig/003_cancer_cell/003_umap_grid.pdf", cancer_grid, width = 20, height = 5)
ggsave("_fig/003_cancer_cell/003_umap_grid_pca.png", cancer_grid_pca, width = 20, height = 5)
ggsave("_fig/003_cancer_cell/003_umap_grid.png", cancer_grid, width = 20, height = 5)# ## downsample objects for exploration
# # set.seed(123)
# # seu_obj_tc_sub <- subset(seu_obj_tc, cells = sample(colnames(seu_obj_tc), 10000))
# # seu_obj_cc_sub <- subset(seu_obj_cc, cells = sample(colnames(seu_obj_cc), 10000))
# seu_obj_cc_sub <- seu_obj_cc
# seu_obj_tc_sub <- seu_obj_tc
#
# tc_data <- cbind(cell_id = colnames(seu_obj_tc_sub), FetchData(seu_obj_tc_sub, c("sample", "cluster_label", names(signature_modules), "CD8.ISG"))) %>%
# filter(str_detect(cluster_label, "CD8")) %>%
# as_tibble %>%
# rename(isabl_id = sample) %>%
# gather(module, tc_score, -cell_id, -isabl_id, -cluster_label) %>%
# left_join(meta_tbl, by = "isabl_id")
#
# patient_tc_ranks <- tc_data %>%
# filter(sort_short == "CD45+", therapy == "pre-Rx") %>%
# group_by(sample_id, module) %>%
# summarise(median_tc_score = median(tc_score)) %>%
# ungroup1 Differential expression in cancer cells
Are there certain pathways activated in cancer cells…
- of patients with high vs low dysfunctional T cells?
- of HRD vs FBI patients
# progeny_tbl <- progeny(as.matrix(seu_obj_cc_sub@assays$RNA@data[VariableFeatures(seu_obj_cc_sub),])) %>%
# as_tibble(rownames = "cell_id") %>%
# gather(pathway, score, -cell_id)
#
# plot_data <- cbind(cell_id = colnames(seu_obj_cc_sub), FetchData(seu_obj_cc_sub, c("umapharmony_1", "umapharmony_2", "sample", "RNA_snn_res.0.2", "nFeature_RNA", "percent.mt"))) %>%
# as_tibble() %>%
# rename(isabl_id = sample) %>%
# left_join(meta_tbl, by = "isabl_id") %>%
# filter(sort_short == "CD45-", therapy == "pre-Rx") %>%
# left_join(select(signature_tbl, patient_id, consensus_signature), by = "patient_id") %>%
# left_join(progeny_tbl, by = "cell_id")
#
# patient_pw_ranks <- plot_data %>%
# group_by(pathway, patient_id) %>%
# summarise(median_score = median(score)) %>%
# ungroup() %>%
# arrange(pathway, median_score) %>%
# # group_by(pathway) %>%
# mutate(pw_rank = as.factor(row_number(median_score))) %>%
# ungroup()
#
# patient_pw_ranks_site <- plot_data %>%
# group_by(pathway, sample_id) %>%
# summarise(median_score_site = median(score)) %>%
# ungroup() %>%
# arrange(pathway, median_score_site) %>%
# # group_by(pathway) %>%
# mutate(pw_rank_site = as.factor(row_number(median_score_site))) %>%
# ungroup()
#
# plot_data_ranked <- plot_data %>%
# left_join(patient_pw_ranks, by = c("pathway", "patient_id")) %>%
# left_join(patient_pw_ranks_site, by = c("pathway", "sample_id"))1.1 pathway scores across cancer cell clusters
# p1 <- ggplot() +
# geom_point(aes(umapharmony_1, umapharmony_2, color = RNA_snn_res.0.2), size = 0.01,
# data = distinct(plot_data, umapharmony_1, umapharmony_2, RNA_snn_res.0.2)) +
# theme_void() +
# guides(color = guide_legend(override.aes = list(size = 2))) +
# coord_fixed()
#
# p2 <- ggplot() +
# geom_bar(aes(RNA_snn_res.0.2, n, fill = RNA_snn_res.0.2), stat = "identity",
# data = distinct(plot_data, umapharmony_1, umapharmony_2, RNA_snn_res.0.2) %>%
# group_by(RNA_snn_res.0.2) %>% tally) +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
# scale_y_log10() +
# guides(fill = F)
#
# p3 <- ggplot() +
# geom_point(aes(umapharmony_1, umapharmony_2), size = 0.01, color = "grey80",
# data = distinct(plot_data, umapharmony_1, umapharmony_2)) +
# geom_point(aes(umapharmony_1, umapharmony_2, color = RNA_snn_res.0.2), size = 0.01, alpha = 0.1,
# data = distinct(plot_data, umapharmony_1, umapharmony_2, RNA_snn_res.0.2)) +
# facet_wrap(~RNA_snn_res.0.2, ncol = 8) +
# theme_void() +
# guides(color = guide_legend(override.aes = list(size = 2))) +
# coord_fixed()
#
# plot_grid(p1, p2, nrow = 1)
# p3# ggplot() +
# geom_point(aes(umapharmony_1, umapharmony_2), size = 0.1, color = "grey80",
# data = filter(plot_data, score <= 0)) +
# geom_point(aes(umapharmony_1, umapharmony_2, color = score), size = 0.1,
# data = filter(plot_data, score > 0)) +
# facet_wrap(~pathway) +
# scale_color_gradientn(colors = viridis(9)) +
# theme_void()
#
# ggplot() +
# geom_boxplot(aes(RNA_snn_res.0.2, score, color = RNA_snn_res.0.2),
# data = plot_data) +
# facet_wrap(~pathway, scales = "free") +
# # scale_color_gradientn(colors = viridis(9)) +
# theme_void() +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
#
# select(markers_tbl, cluster, gene) %>%
# group_by(cluster) %>%
# mutate(rank = row_number(cluster)) %>%
# slice(1:20) %>%
# spread(cluster, gene) %>%
# knitr::kable()1.2 pathway scores across tumor sites
# ggplot() +
# geom_boxplot(aes(tumor_supersite, score, color = tumor_supersite),
# data = plot_data_ranked) +
# facet_wrap(~pathway, scales = "free_y") +
# scale_color_manual(values = clrs$tumor_supersite) +
# theme_void()
#
# ggplot() +
# geom_boxplot(aes(pw_rank_site, score, color = tumor_supersite, group = isabl_id),
# data = plot_data_ranked) +
# facet_wrap(~pathway, scales = "free") +
# scale_color_manual(values = clrs$tumor_supersite) +
# theme_void()1.3 pathway scores across mutational signatures
# ggplot() +
# geom_boxplot(aes(consensus_signature, score, color = consensus_signature),
# data = plot_data) +
# facet_wrap(~pathway, scales = "free_y") +
# scale_color_manual(values = clrs$consensus_signature) +
# theme_void()
#
# ggplot(plot_data_ranked) +
# geom_boxplot(aes(pw_rank, score, color = consensus_signature, group = patient_id)) +
# facet_wrap(~pathway, scales = "free") +
# scale_color_manual(values = clrs$consensus_signature) +
# scale_x_discrete(breaks = arrange(distinct(plot_data_ranked, pw_rank, patient_id_short), pw_rank)$pw_rank,
# labels = arrange(distinct(plot_data_ranked, pw_rank, patient_id_short), pw_rank)$patient_id_short) +
# theme_void() +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))1.4 correlation of pathway scores and T cell scores
# tc_cc_cor_tbl <- patient_pw_ranks_site %>%
# left_join(patient_tc_ranks, by = "sample_id") %>%
# left_join(distinct(meta_tbl, patient_id, sample_id, tumor_supersite),
# by = "sample_id") %>%
# left_join(select(signature_tbl, patient_id, consensus_signature),
# by = "patient_id") %>%
# na.omit()
#
# ggplot(tc_cc_cor_tbl) +
# geom_point(aes(median_tc_score, median_score_site, color = tumor_supersite),
# size = 0.1) +
# facet_grid(module~pathway) +
# scale_color_manual(values = clrs$tumor_supersite) +
# stat_cor(aes(median_tc_score, median_score_site), method = "spearman",
# label.sep = "\n") +
# geom_smooth(aes(median_tc_score, median_score_site),
# method = "lm", color = "black")
#
# ggplot(tc_cc_cor_tbl) +
# geom_point(aes(median_tc_score, median_score_site, color = consensus_signature),
# size = 0.1) +
# facet_grid(module~pathway) +
# scale_color_manual(values = clrs$consensus_signature) +
# stat_cor(aes(median_tc_score, median_score_site), method = "spearman",
# label.sep = "\n") +
# geom_smooth(aes(median_tc_score, median_score_site),
# method = "lm", color = "black")2 JAK-STAT signaling
# p1 <- ggplot() +
# geom_boxplot(aes(consensus_signature, score, color = consensus_signature),
# data = filter(plot_data, pathway == "JAK-STAT")) +
# facet_wrap(~pathway, scales = "free_y") +
# scale_color_manual(values = clrs$consensus_signature) +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
# guides(color = F) +
# labs(x = "", y = "PROGENy score")
#
# plot_data_ranked_jak_stat <- filter(plot_data_ranked, pathway == "JAK-STAT")
#
# p2 <- ggplot(plot_data_ranked_jak_stat) +
# geom_boxplot(aes(pw_rank, score, color = consensus_signature, group = patient_id)) +
# facet_wrap(~pathway, scales = "free") +
# scale_color_manual(values = clrs$consensus_signature) +
# scale_x_discrete(breaks = arrange(distinct(plot_data_ranked_jak_stat, pw_rank, patient_id_short), pw_rank)$pw_rank,
# labels = arrange(distinct(plot_data_ranked_jak_stat, pw_rank, patient_id_short), pw_rank)$patient_id_short) +
# theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
# guides(color = F) +
# labs(x = "", y = "PROGENy score", color = "Signature")
#
# p3 <- ggplot(filter(tc_cc_cor_tbl, pathway == "JAK-STAT", module == "CD8.ISG")) +
# geom_point(aes(median_tc_score, median_score_site, color = consensus_signature),
# size = 1) +
# facet_wrap(~pathway) +
# scale_color_manual(values = clrs$consensus_signature) +
# stat_cor(aes(median_tc_score, median_score_site), method = "spearman",
# label.sep = ", ") +
# geom_smooth(aes(median_tc_score, median_score_site),
# method = "lm", color = "black") +
# labs(x = "ISG module score\nin CD8 T cells",
# y = "JAK-STAT PROGENy\nscore in cancer cells",
# color = "Signature") +
# theme(strip.text = element_blank())
#
# plot_grid(plot_grid(p1, p2, align = "h", rel_widths = c(0.3, 0.7)),
# p3, rel_widths = c(0.6, 0.4))3 session info
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2020-12-11
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date lib
## abind 1.4-5 2016-07-21 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## bitops 1.0-6 2013-08-17 [2]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## desc 1.2.0 2018-05-01 [2]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggpubr * 0.4.0 2020-06-27 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## ggsignif 0.6.0 2019-08-08 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.4.0 2019-10-04 [2]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## labeling 0.3 2014-08-23 [2]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## P magick * 2.4.0 2020-06-23 [?]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## openxlsx 4.1.5 2020-05-06 [1]
## pbapply 1.4-2 2019-08-31 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppParallel 4.4.4 2019-09-27 [2]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rio 0.5.16 2018-11-26 [1]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## rstatix 0.6.0 2020-06-18 [1]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## sn 1.5-4 2019-05-14 [2]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
## tsne 0.1-3 2016-07-15 [2]
## usethis 1.5.1 2019-07-04 [2]
## uwot 0.1.5 2019-12-04 [2]
## vctrs 0.3.5 2020-11-17 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zoo 1.8-7 2020-01-10 [2]
## source
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Github (clauswilke/colorblindr@1ac3d4d)
## R-Forge (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library
##
## P ── Loaded and on-disk path mismatch.